International Review of Law and Economics最新文献

This study examines the effectiveness of Germany's reformed asset recovery regime, which was implemented in 2017, in terms of its ability to confiscate proceeds of crime and whether it qualifies as illicit enrichment legislation. The research utilizes Dornbierer's (2021) definition of illicit enrichment to evaluate the reformed asset recovery law and analyses trends in asset recovery by reviewing data on assets seized and confiscated since 2017. Additionally, the study compares the reformed asset recovery regime to its predecessor to determine whether weaknesses that reduced the effectiveness of the previous framework to confiscate PoC have been addressed, while also evaluating the reformed regime for any potential weaknesses that may hinder its ability to confiscate proceeds of crime. The study concludes that while the reformed regime introduces some elements of illicit enrichment, it does not satisfy the criteria for illicit enrichment legislation. Nonetheless, the reformed regime is more effective in confiscating proceeds of crime, as evidenced by the high value of assets seized since the reform was implemented. Additionally, most of the weaknesses that existed in the previous system have been resolved. However, the research highlights the remaining challenges regarding the confiscation of proceeds implicated in ML, fraud, and corruption, as well as profits from non-criminal offenses. Future studies could explore whether the increased confiscation of assets leads to a decrease in profit-driven crime.

This paper analyzes how the plaintiff selects her lawyer based on lawyers’ confidence in their trial-effort productivity. The plaintiff’s lawyer works on a contingent fee and makes litigation decisions on the plaintiff’s behalf. When the lawyer’s preferences are decisive at both the settlement and the trial stage, the plaintiff must anticipate that a more confident lawyer evaluates settlement compared to trial differently and implies different equilibrium trial effort levels. When the lawyer implements the plaintiff’s ideal settlement demand, only the influence of the confidence level on trial effort levels is relevant. In both cases, the plaintiff prefers an overconfident lawyer but would be harmed by excessive overconfidence. In many circumstances, the optimal confidence level maximizes the plaintiff’s trial payoff. However, when the lawyer’s preferences are decisive at both the settlement and trial stage, the plaintiff may choose an even more confident lawyer to raise the settlement level her lawyer demands from the defendant.

The "law and finance" paradigm posits that legal institutions play a crucial role in financial development; however, China has long been considered an exception. This study challenges that assumption by examining how improvements in the judiciary affect financial development in China. Using a quasi-natural experiment (i.e., staggered difference-in-difference estimation) over a twenty-year period, we find that the establishment of specialized financial adjudication institutions (i.e., financial courts and tribunals) in certain prefecture-level cities significantly reduces financing constraints for local listed companies. Further heterogeneity tests show that these effects are more pronounced among private enterprises, small and medium-sized enterprises, and companies in the central and western regions. Through the analysis of representative practices and interviews with relevant judges and enterprises, we find that China's financial judiciary demonstrates efficiency and proactiveness. Additionally, political considerations enable courts to regulate finance and maintain stability, improving the local financial legal environments and reducing transaction costs for market participants. By investigating the causal relationship between judicial reforms and financial development, our findings provide new insights into the "law and finance" theory and offer policy implications for addressing financial development gaps and promoting financial inclusion in emerging markets.

We use machine learning methods to predict which patents end up in court using the population of US patents granted between 2002 and 2005. We show that patent characteristics have significant predictive power, particularly value indicators and patent-owner characteristics. Furthermore, we analyze the predictive performance concerning the number of observations used to train the model, which patent characteristics to use, and which predictive model to choose. We find that extending the set of patent characteristics has the biggest positive impact on predictive performance. The model choice matters as well. More sophisticated machine learning methods provide additional value relative to a simple logistic regression. This result highlights the existence of non-linearities among and interactions across the predictors. Our results provide practical advice to anyone building patent litigation models, e.g., for litigation insurance or patent management more generally.